Melanie Riley

Melanie Riley

Machine Learning Engineer at Fetch

Melanie is a Machine Learning Engineer passionate about human-centered design. She uses machine learning techniques to leverage data for solving human problems. She enjoys exercising both analytical and creative thinking to develop solutions across a variety of industries.

Watch live: May 8, 2024 @ 12:40 – 1:10 pm ET

Optimizing Sentence Transformers for Entity Resolution at Scale

At Fetch, we reward our users for snapping pictures of their receipts. Each day, this happens over 11 million times. Our machine learning and engineering teams are hard at work building systems that extract, normalize, and enrich information from these receipts as accurately and quickly as possible. One of the most important steps in this process is entity resolution. Entity resolution is the process of identifying and linking records that correspond to the same entity across different data sources. Paper receipts have diverse conventions for representing important text entities such as the originating business or “retailer”, purchased product descriptions, and payment information. In this talk, we will cover conception to deployment and discuss how we adapt popular sentence transformers and approximate nearest neighbor algorithms to our unique domain of receipt-language. We will discuss how we optimized the models for real-time production workloads and deployed the models to our 18 million MAU. Today the system makes inferences on over 11 million receipts uploaded each day. We also use Comet ML to track our model experiments